import torch.optim
import torchvision
from jinja2.optimizer import optimize
from torch.utils.tensorboard import SummaryWriter

from model import *
from torch.utils.data import DataLoader

from model_pretrained import train_data
from test_tb import writer

#准备数据集
train_data=torchvision.datasets.CIFAR10("./dataset",train=True,transform=torchvision.transforms.ToTensor(),
                                        download=True)
test_data=torchvision.datasets.CIFAR10("./dataset",train=True,transform=torchvision.transforms.ToTensor(),
                                        download=True)

#length长度
train_data_size=len(train_data)
test_data_size=len(test_data)

#如果train_data_size=10，训练数据集的长度为:10
print("训练数据集的长度为:{}".format(train_data_size))
print("训练数据集的长度为:{}".format(test_data_size))

#利用DataLoder来加载数据集
train_dataloader=DataLoader(train_data,batch_size=64)
tset_dataloader=DataLoader(test_data,batch_size=64)

#创建网络模型
tudui=Tudui()

#损失函数
loss_fn=nn.CrossEntropyLoss()

#优化器
#1e-2=1/1^(-2)=1/100=0.01
learning_rate=1e-2
#SGD向下优化
optimizer=torch.optim.SGD(tudui.parameters(),lr=learning_rate)

#设置训练网络的一些参数
#记录训练次数
total_train_step=0
#记录测试次数
total_test_step=0
#训练的轮数
opoch=10

#添加tensorboard
writer=SummaryWriter("./logs_train")
for i in range(opoch):
    print("---------第{}轮训练开始--------".format(i+1))

    #训练步骤开始
    for data in train_dataloader:
        imgs,targets=data
        outputs=tudui(imgs)
        loss=loss_fn(outputs,targets)

        #优化器优化模型
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if total_train_step%100==0:
            total_train_step=total_train_step+1
            #加上item结果变成一个数
            print("训练次数:{},Loss:{}".format(total_train_step,loss.item()))
            writer.add_scalar("train_loss",loss.item(),total_train_step)


    #测试步骤开始
    total_test_loss=0
    total_accuracy=0
    #将梯度删除
    with torch.no_grad():
        for data in tset_dataloader:
            imgs,targets=data
            outputs=tudui(imgs)
            loss=loss_fn(outputs,targets)
            total_test_loss=total_test_loss+loss.item()
            accuracy=(outputs.argmax(1)==targets).sum()
            total_accuracy=total_accuracy+accuracy

        print("整体测试集上的Loss:{}".format(total_test_loss))
        print("整体测试集上的正确率:{}".format(total_accuracy/test_data_size))
        writer.add_scalar("test_loss",total_test_loss,total_test_step)
        #test_accuracy测试的正确率
        writer.add_scalar("test_accuracy", total_accuracy/test_data_size, total_test_step)
        total_train_step=total_train_step+1


        torch.save(tudui,"tudui_{}.pth".format(i))
        print("模型已经保存成功")

writer.close()

